快速成型技术中分层算法的研究与进展

根据对零件制造精度和效率的关注程度的不同,开发出了多种分层算法。在同等加工时间的情况下,根据加工精度的不同,将这些分层算法分为等层厚分层算法和适应性分层算法两类。通过对STL模型、原始CAD模型和点云数据的分析,讨论了两类分层算法的研究和发展,然后介绍了斜边分层算法和曲面分层算法等先进分层算法的原理和成果,最后讨论了快速成型分层算法的研究方向和趋势。

卷烟条盒包装质量检测机器视觉系统设计

为了避免人眼对产品外观质量检测时,人眼视觉疲劳所产生的产品错检、漏检,提出了基于机器视觉的烟支条盒包装质量检测系统的设计。该系统由光源、相机、图像采集卡和工业控制计算机组成视觉系统处理单元,应用定位配准、边缘检测和斑点分析等图像处理算法完成对产品缺陷的检测。该视觉检测系统已经在烟厂得到了实际应用,对今后开发类似的基于机器视觉的产品外观质量检测系统具有一定的参考价值和指导意义。

An in silico approach for screening flavonoids as P-glycoprotein inhibitors based on a Bayesian-regularized neural network

P-glycoprotein (P-gp), an ATP-binding cassette (ABC) transporter, functions as a biological barrier by extruding cytotoxic agents out of cells, resulting in an obstacle in chemotherapeutic treatment of cancer. In order to aid in the development of potential P-gp inhibitors, we constructed a quantitative structure-activity relationship (QSAR) model of flavonoids as P-gp inhibitors based on Bayesian-regularized neural network (BRNN). A dataset of 57 flavonoids collected from a literature binding to the C-terminal nucleotide-binding domain of mouse P-gp was compiled. The predictive ability of the model was assessed using a test set that was independent of the training set, which showed a standard error of prediction of 0.146 +/- 0.006 (data scaled from 0 to 1). Meanwhile, two other mathematical tools, back-propagation neural network (BPNN) and partial least squares (PLS) were also attempted to build QSAR models. The BRNN provided slightly better results for the test set compared to BPNN, but the difference was not significant according to F-statistic at p = 0.05. The PLS failed to build a reliable model in the present study. Our study indicates that the BRNN-based in silico model has good potential in facilitating the prediction of P-gp flavonoid inhibitors and might be applied in further drug design.